An Empirical Study on Auditor Switching Behavior and Its Determinants in Listed Companies

Posted: Dec 25, 2014

• Riley Morris
• Nora Pearson
• Ella Bryant

Abstract

The phenomenon of auditor switching represents a critical junction in corporate governance where financial reporting integrity, regulatory compliance, and stakeholder confidence intersect. Traditional accounting research has predominantly approached auditor switching through the lens of agency theory and economic determinants, focusing on factors such as audit fees, opinion shopping, and corporate financial performance. However, the increasing complexity of modern business environments, coupled with the digital transformation of corporate operations, necessitates a more sophisticated analytical framework that can capture the multi-dimensional nature of auditor-client relationships. This research introduces a computational paradigm shift in studying auditor switching behavior by integrating techniques from machine learning, network science, and natural language processing. Our approach moves beyond the limitations of conventional statistical methods that often assume linear relationships and independent observations. Instead, we conceptualize auditor switching as a complex system phenomenon influenced by interconnected factors operating across multiple temporal and organizational scales. We address three fundamental research questions that have remained inadequately explored in existing literature. First, how do latent patterns in corporate governance structures and communication networks influence auditor switching decisions? Second, to what extent can computational signatures derived from unstructured corporate data predict switching behavior with greater accuracy than traditional financial metrics? Third, what network effects and contagion mechanisms characterize auditor switching across industry sectors and corporate relationship networks?